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A Spectral AutoML approach for industrial soft sensor development: Validation in an oil refinery plant
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2021-04-20 , DOI: 10.1016/j.compchemeng.2021.107324
Daniela C.M. de Souza , Luís Cabrita , Cláudia F. Galinha , Tiago J. Rato , Marco S. Reis

Spectral AutoML is a platform for fast development of PAT soft sensors that considers the combined effect of pre-processing, band selection, band-wise resolution definition, hyper-parameter tuning and model estimation. Spectral AutoML was compared with models developed under the classic paradigm, and their performance assessed on an independent test set. The validation study regards the prediction of 12 different diesel fuels properties, using FTIR-ATR spectra. The proposed framework led to clearly better predictions in 8 out of the 12 properties, and minor improvements in 3 properties. The Spectral AutoML results were obtained overnight, without interfering in the daily work of the users, while the benchmark models resulted from several months of work and fine tuning of the methods. The results demonstrated the added value of the proposed Spectral AutoML approach in terms of prediction accuracy, development time of the models and reduced dependence on resident experts.



中文翻译:

用于工业软传感器开发的Spectral AutoML方法:在炼油厂中的验证

Spectral AutoML是用于快速开发PAT软传感器的平台,该平台考虑了预处理,波段选择,按波段分辨率定义,超参数调整和模型估计的综合效果。将Spectral AutoML与在经典范式下开发的模型进行了比较,并在独立的测试集上评估了它们的性能。验证研究使用FTIR-ATR光谱预测了12种不同的柴油特性。拟议的框架导致对12个属性中的8个进行了明显更好的预测,并对3个属性进行了较小的改进。Spectral AutoML结果是在一夜之间获得的,而不会影响用户的日常工作,而基准模型则是经过数月的工作和方法的精细调整而得出的。

更新日期:2021-04-26
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